vastai-performance-tuning

Optimize Vast.ai GPU instance selection, startup time, and training throughput. Use when optimizing instance selection, reducing startup latency, or maximizing GPU utilization on rented hardware. Trigger with phrases like "vastai performance", "optimize vastai", "vastai slow", "vastai gpu utilization", "vastai throughput".

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Repositorio: jeremylongshore/claude-code-plugins-plus-skills
Última actividad en el origen: 17 de julio de 2026 a las 22:18
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name	vastai-performance-tuning
description	Optimize Vast.ai GPU instance selection, startup time, and training throughput. Use when optimizing instance selection, reducing startup latency, or maximizing GPU utilization on rented hardware. Trigger with phrases like "vastai performance", "optimize vastai", "vastai slow", "vastai gpu utilization", "vastai throughput".
allowed-tools	Read, Write, Edit, Bash(vastai:), Bash(ssh:)
version	1.11.0
license	MIT
author	Jeremy Longshore <jeremy@intentsolutions.io>
tags	["saas","vast-ai","api","performance"]
compatibility	Designed for Claude Code, also compatible with Codex and OpenClaw

Vast.ai Performance Tuning

Overview

Optimize GPU instance selection, startup time, and training throughput on Vast.ai. Key levers: Docker image caching, GPU selection by dlperf score, data pipeline optimization, and multi-GPU scaling.

Prerequisites

Vast.ai account with active or planned instances
Understanding of GPU compute bottlenecks
Profiling tools (nvidia-smi, torch.profiler)

Instructions

Step 1: Optimize Instance Selection by Performance

# Sort by dlperf (deep learning performance benchmark) instead of price
vastai search offers 'num_gpus=1 gpu_ram>=24 reliability>0.95' \
  --order 'dlperf-' --limit 10

# The dlperf field measures actual GPU compute throughput
# Higher dlperf = faster training even at same GPU model
# Variance within same GPU model can be 20-30%

def select_by_performance_per_dollar(offers):
    """Select the offer with best performance per dollar."""
    for o in offers:
        o["perf_per_dollar"] = o.get("dlperf", 0) / max(o["dph_total"], 0.01)
    return max(offers, key=lambda o: o["perf_per_dollar"])

Step 2: Reduce Instance Startup Time

# Use smaller, pre-cached Docker images
# FAST: nvidia/cuda:12.1.1-runtime-ubuntu22.04 (~2GB, widely cached)
# MEDIUM: pytorch/pytorch:2.2.0-cuda12.1-cudnn8-runtime (~4GB)
# SLOW: custom-image:latest with pip install at build (~10GB+)

# Pre-install deps in the image, not in onstart
# BAD (slow startup):
vastai create instance $ID --image pytorch/pytorch:latest \
  --onstart-cmd

# Profile GPU utilization on the instance
# SSH into instance and run:
"""
watch -n 1 nvidia-smi  # Check if GPU util is <80% → data bottleneck

# Common fixes for low GPU utilization:
# 1. Increase DataLoader num_workers
# 2. Use pin_memory=True
# 3. Pre-fetch data to local SSD (not NFS)
# 4. Use WebDataset or FFCV for streaming datasets
"""

# Optimize PyTorch DataLoader
from torch.utils.data import DataLoader

loader = DataLoader(
    dataset,
    batch_size=32,
    num_workers=4,       # Match CPU cores on instance
    pin_memory=True,     # Faster GPU transfer
    prefetch_factor=2,   # Pre-load 2 batches per worker
    persistent_workers=True,  # Don't respawn workers each epoch
)

# Check available VRAM before selecting batch size
import torch

def optimal_batch_size(model, sample_input, gpu_memory_gb):
    """Binary search for largest batch size that fits in VRAM."""
    lo, hi, best = 1, 512, 1
    while lo <= hi:
        mid = (lo + hi) // 2
        try:
            torch.cuda.empty_cache()
            batch = sample_input.repeat(mid, *([1] * (sample_input.dim() - 1)))
            _ = model(batch.cuda())
            best = mid
            lo = mid + 1
        except torch.cuda.OutOfMemoryError:
            hi = mid - 1
        torch.cuda.empty_cache()
    return best

# Search for multi-GPU offers (NVLink preferred for training)
vastai search offers 'num_gpus>=4 gpu_name=A100 total_flops>=100' \
  --order 'dph_total' --limit 5

# Use torchrun for distributed training
ssh -p $PORT root@$HOST "torchrun --nproc_per_node=4 train.py --batch-size 128"

GPU	VRAM	FP16 TFLOPS	Typical $/hr	Best For
RTX 4090	24GB	82.6	$0.15-0.30	Fine-tuning, inference
A100 40GB	40GB	77.97	$0.80-1.50	Training medium models
A100 80GB	80GB	77.97	$1.00-2.00	Training large models
H100 SXM	80GB	267	$2.50-4.00	High-throughput training

Error	Cause	Solution
Low GPU utilization (<50%)	Data pipeline bottleneck	Increase `num_workers`, use `pin_memory`
OOM during training	Batch size too large	Use `optimal_batch_size()` or gradient accumulation
Slow instance startup	Large Docker image	Pre-install deps in image, not onstart
Poor multi-GPU scaling	Communication bottleneck	Use NVLink-connected GPUs, reduce sync frequency